NAM extracts features from each variable using a simple feature net.
Contrary to recent trends, NAM has produced good results with tabular data, despite being a lightweight network [1]}, [2]}.
Thus, we employed the basic premise of NAM and expanded it to handle NC tasks by employing multi-task learning to support predictions based on multivariate time-series data.
